Quantitative Researcher

About the Role You will play a key role in uncovering alpha opportunities across asset classes, leveraging your expertise in machine learning, optimization, and other innovative advanced data-driven techniques. This is a full time role based out of our Boston office. The candidate will be required to follow the firm’s rule on Work from Office (3 days).  About the Team Loomis Sayles' Systematic Investing Strategies (SIS) team seeks a talented and passionate Quantitative Researcher to join our dynamic group. We are a highly collaborative, data-driven, intellectually rigorous team responsible for coming up with investment strategies, programming those hypothesis into signals, simulating a back-test of the signals, and producing alpha, risk and trading cost forecasts based on the signals to drive trading decisions. We maintain a friendly, team-oriented environment and place a high value on professionalism, attitude and entrepreneurship. Job Responsibilities Craft innovative strategies using advanced mathematical techniques, primarily in derivatives markets (futures, forwards, CDX, swaps) Perform exploratory data analysis on structured and unstructured data to identify alpha drivers and build robust signals Performing ad-hoc exploratory statistical analysis across multiple large complex data sets Explore and integrate text data into your research to drive alpha generation Apply machine learning techniques (deep, reinforcement, causal) to build and refine monetization systems for trading signals Attend industry events and conferences to stay ahead of the curve and gain local market insights. Contribute to the growth of the Multi-Asset Alpha signal generation team Develop and maintain client marketing materials, effectively communicating complex concepts to diverse audiences Qualifications & Education Requirements PhD in a relevant field (Physics, Math, Engineering, Chemistry, Statistics) Candidates with 2-5 years of buy-side or sell side experience from top-tier universities preferred Technical expertise: Proficiency in machine learning (deep/reinforcement/causal), linear and non-linear optimization, and Bayesian statistics Programming: Strong analytical programming skills, with a preference for MATLAB and Python Personal qualities: Highly motivated, detail-oriented, and able to collaborate and communicate effectively across different levels Data-driven mindset: Prior experience in a data-driven analytical research environment is a plus Loomis Sayles Benefit Overview